LLM
Fine-tuning

We collect and clean your proprietary documents, conversational logs, and files to prepare training datasets.

We select the appropriate open-source base model (like Llama 3 or Mistral) based on performance, size, and cost requirements.

We apply Supervised Fine-Tuning (SFT) using LoRA or QLoRA on high-performance GPUs, optimizing hyperparameters carefully.

We align model responses using DPO (Direct Preference Optimization) to match your target tone of voice and enforce security guardrails.

We run automated benchmark tests against a validation dataset to measure accuracy, formatting compliance, and hallucinations.

We deploy the fine-tuned model inside a private cloud or on-premise hardware using vLLM for optimized inference speed.